Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> The 2017 National Academy of Sciences Decadal Survey highlighted several high-priority objectives to be pursued in the decadal timeframe, and the next-generation Cloud, Convection and Precipitation (CCP) observing system is thereby contemplated. In this study, we develop a suite of hybrid Bayesian algorithms to evaluate two CCP remote sensor candidates including a W-band cloud radar and a (sub)millimeter-wave radiometer with channels in the 118â880âGHz frequency range for capability in constraining ice cloud microphysical quantities. The algorithms address active-only, passive-only, and synergistic activeâpassive retrievals. The hybrid Bayesian algorithms combine the Bayesian Monte Carlo integration and optimization process to retrieve quantities with uncertainty estimates. The radar-only retrievals employ the optimal estimation methodology, while the radiometer-involved retrievals employ ensemble approaches to maximize the posterior probability density function. A priori information is obtained from the Tropical Composition, Cloud and Climate Coupling (TC4) in situ data and CloudSat radar observations. End-to-end simulation experiments are conducted to evaluate the retrieval accuracies by comparing the retrieved parameters with known values. The experiment results suggest that the radiometer measurements possess high sensitivity to ice cloud particles with large water content. The radar-only retrievals demonstrate capability in reproducing ice water content profiles, but the performance in retrieving number concentration is poor. The synergistic observations enable improved pixel-level retrieval accuracies, and the improvements in ice water path retrievals are significant. The proposed retrieval algorithms could serve as alternative methods for exploring the synergistic active and passive concept, and the algorithm framework could be extended to the inclusion of other remote sensors to further assess the CCP observing system in future studies.
Highlights
The 2017 National Academy of Sciences Decadal Survey (National Academies of Sciences, Engineering, and Medicine, 2018) identified five designated foundational observations to be pursued during the 2017–2027 time frame, including aerosols (A), clouds, convection, and precipitation (CCP) as designated observables (DOs)
The retrieval results again illustrate that the radar measurements are much more sensitive to the ice water content (IWC) variation compared to the number concentration (NC) variation
We develop a suite of hybrid Bayesian retrieval algorithms to assess a candidate observing system representative of what is being considered for the decadal survey clouds-convection-precipitation designated observable mission to be flown later this decade
Summary
The 2017 National Academy of Sciences Decadal Survey (National Academies of Sciences, Engineering, and Medicine, 2018) identified five designated foundational observations to be pursued during the 2017–2027 time frame, including aerosols (A), clouds, convection, and precipitation (CCP) as designated observables (DOs). The A and CCP DOs were merged to exploit synergies in the measurement systems. The objective of the preformulation study was to identify measurables that can achieve the science objectives of the DOs. As such, the study identified observing system architectures that maximize science benefits while limiting cost and risk. To narrow in on a set of viable architectures, the ACCP study relied on a suite of observing system simulation experiments (OSSEs) aimed at addressing pixel-level retrieval uncertainties and sampling trade-offs for various geophysical variables that were deemed important for achieving science goals. The properties of ice clouds are among the critical geophysical variables in the CCP science objectives. Studies suggest that ice clouds are a net heat source to the climate system (Ackerman et al, 1988; Berry and Mace, 2014) while contributing positive feedback to the climate system (Zelinka and Hartmann, 2011)
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